• Korean Management Science Review
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• v.25 no.1
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• pp.29-41
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• 2008

In the mobile communication service market, this study represents an attempt to forecast the subscribers of the IMT-2000 service market using the questionnaire of experts which is the qualitative technique is used. In this study, by using the substitution model of next generations among products in order to analyze the IMT-2000 demand of service, a demand was predicted. And by estimating the market demand prospect in which it becomes the important factor of the IMT-2000 service diffusion according to each bandwidth frequency the politically necessary approaching direction about the frequency was presented. It will be able to become the important part to not only the business carrier but also the policy maker to examine a prospect toward the subscriber of the IMT-2000 service. As a result, the market demand was exposed to be most big when the SKT 800MHz, and the KTF 800(900)MHz were used as the additional frequency. And it was likely to reach to the IMT-2000 number of subscribers to about 35.750 thousand peoples in the future at 2015.

• Informatization Policy
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• v.27 no.4
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• pp.24-46
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• 2020

This study proposes a way to timely forecast open government data (OGD) demand(i.e., OGD requests, search queries, etc.) by using keyword network analysis. According to the analysis results, most of the OGD belonging to the high-demand topics are provided by the domestic OGD portal(data.go.kr), while the OGD related to users' actual needs predicted through topic association analysis are rarely provided. This is because, when providing(or selecting) OGD, relevance to OGD topics takes precedence over relevance to users' OGD requests. The proposed keyword network analysis framework is expected to contribute to the establishment of OGD policies for public institutions in the future as it can quickly and easily forecast users' demand based on actual OGD requests.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.345-352
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• 2022

Conducting AI-based fusion business due to the increment of ICT fusion medical device has been expanded. In addition, AI-based medical devices help change existing medical system on treatment into the paradigm of customized treatment such as preliminary diagnosis and prevention. It will be generally promoted to the change of medical device industry. Although the current demand forecasting of medical biotechnology commercialization is based on the method of Delphi and AHP, there is a problem that it is difficult to have a generalization due to fluctuation results according to a pool of participants. Therefore, the purpose of the paper is to predict demand forecasting for identifying promising technology based on building up big data in medical biotechnology. The development method is to employ candidate technologies of keywords extracted from SCOPUS and to use word2vec for drawing analysis indicator, technological distance similarity, and recommended technological similarity of top-level items in order to achieve a reasonable result. In addition, the method builds up academic big data for 5 years (2016-2020) in order to commercialize technology excavation on demand perspective. Lastly, the paper employs global data studies in order to develop domestic and international demand for technology excavation in the medical biotechnology field.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1276-1280
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• 2015

Among the various statistical factors for South Korea, the population has been steadily decreased by lower birthrate. Nevertheless, the number of household is constantly increasing amid population aging and single life style. In general, residential electricity use is more the result of the number of household than the population. Therefore, residential electricity consumption is expected to be far higher for decades to come. The existing long-term load forecasting, however, do not necessarily reflect the growth of single and two-member households. In this respect, this paper proposes the long-term load forecasting for residential users considering the effect of changes of the housing type, and in the case study the changes of the residential load pattern is analyzed for accurate long-term load forecasting.

• IE interfaces
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• v.18 no.3
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• pp.333-342
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• 2005

In this paper, we propose an efficient forecasting methodology of the mid and long-term frequency demand in Korea. The methodology consists of the following three steps: classification of basic service group, calculation of effective traffic, and frequency forecasting. Based on the previous studies, we classify the services into wide area mobile, short range radio, fixed wireless access and digital video broadcasting in the step of the classification of basic service group. For the calculation of effective traffic, we use the measures of erlang and bps. The step of the calculation of effective traffic classifies the user and basic application, and evaluates the effective traffic. Finally, in the step of frequency forecasting, different methodology will be proposed for each service group and its applications are presented.

• Journal of Environmental Science International
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• v.19 no.10
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• pp.1229-1235
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• 2010

Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.859-870
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• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.63-72
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• 2012

The Korean telecommunications market has been expanding swiftly, these days, to be saturated. In this environment, the upcoming mobile telecommunication market, where 4G service was introduced this year, is becoming more substitutive and competitive. Thus, the demand forecasting of 4G service is very difficult, while it is critical to market success. This paper adopts a multi-generation diffusion model to capture the diffusion and substitution patterns for two successive generation of technological services, i.e., 3G and 4G mobile telecommunications services. The three parameters, i.e., the coefficient of innovation, the coefficient of imitation, and the coefficient of market potential, used in the multi-generation diffusion model based on Norton and Bass[11] are obtained by inference from similar substitutive relations between older and newer telecommunication services to 3G and 4G services. The simulation results show that the Bass type multi-generation model can be successfully applied to the demand forecasting of newly introduced 4G mobile telecommunication service.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.179-196
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• 2021

Port congestion rate at Busan Port has increased for three years. Port congestion causes container reconditioning, which increases the dockyard labor's work intensity and ship owner's waiting time. If congestion is prolonged, it can cause a drop in port service levels. Therefore, this study proposed an anomaly detection method using ARIMA(Autoregressive Integrated Moving Average) model with the daily volume data from 2013 to 2020. Most of the research that predicts port volume is mainly focusing on long-term forecasting. Furthermore, studies suggesting methods to utilize demand forecasting in terms of port operations are hard to find. Therefore, this study proposes a way to use daily demand forecasting for port anomaly detection to solve the congestion problem at Busan port.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.109-115
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• 2019

Most demand forecasting studies for telecommunication services have focused on estimating market size at the introductory stage of new products or services, or on suggesting improvement methods of forecasting models. Although such studies forecast business growth and market sizes through demand forecasting for new technologies and overall demands in markets, they have not suggested more specific information like relative market share, customers' preferences on technologies or service, and potential sales power. This study focuses on the telecommunication service industry and explores ways to calculate the relative market shares between competitors, considering competitive situations at the introductory stage of a new mobile telecommunication service provider. To reflect the competitive characteristics of the telecommunication markets, suggested is an extended conjoint analysis using service coverage and service switching rates as modification variables. This study is considered to be able to provide strategic implications to businesses offering existing service and ones planning to launch new services. The result of analysis shows that the new service provider has the greatest market share at the competitive situation where the new service covers the whole country, offers about 50% of existing service price, and allows all cellphones except a few while the existing service carrier maintains its price and service and has no response to the new service introduction. This means that the market share of the new service provider soars when it is highly competitive with fast network speed and low price.